Overview

Row

Confirmed Cases in CARICOM

20580

Active Cases in CARICOM

7743

Total Recoveries in CARICOM

12422

Confirmed Deaths in CARICOM

415

Map

Column

Spatial Distribution of COVID-19

Column

Confirmed Cases among CARICOM Member States

Daily Increase in Cumulative Cases among Worst Affected CARICOM Member States

Explore Relationships

Column

Mortality and Recovery by Income and Economy Type (CARICOM)

economy mean(confirmed_per_100k) mean(mortality_rate) mean(recovery_rate)
Commodity Based 113.6169 1.685873 50.06098
Service Based 168.5244 1.377555 77.56702
income mean(confirmed_per_100k) mean(mortality_rate) mean(recovery_rate)
High income 170.85762 2.1612144 73.71923
Low income 72.57342 2.4590164 70.25936
Upper middle income 158.46971 0.8682046 70.57058
oecs mean(confirmed_per_100k) mean(mortality_rate) mean(recovery_rate)
Non-OECS Member State 243.4860 2.1274045 53.44818
OECS Member State 41.1219 0.5319149 95.97245

Box-plot Comparison of Confirmed Cases per 100k by Income Group (World)

Column

Recovery and Mortality Rate (CARICOM)

Relationship Between Confirmed Cases per 100k (Log Scale) and Population Aged 65 + (World)

Impact of Restrictions

Row

Residential

Recreational Areas

Workplaces

Public Parks

Transit Stations

Groceries and Pharmacies

Confirmed

Column

Share of Confirmed Cases across CARICOM Member States

Column

Per 100,000 in Commodity Based Countries

Per 100,000 in Service Based Countries

Per 100,000 in OECS Member State

Deaths

Column

Share of Confirmed Cases across CARICOM Member States

Column

Per 100k among Commodity Based Countries

Per 100k among Service Based Countries

Per 100k among Service Based Countries

About

Overview

This Dashboard was created in partial fulfilment of the Developing Data Products Course which comprises one of the five courses necessary for the Data Science: Statistics and Machine Learning Specialization offered by Johns Hopikins University through Coursera. This assignment challenged candidates to Create a data product and a reproducible pitch. Once completed, candidates were required to host their webpage on either GitHub Pages, RPubs, or NeoCities. The webpage presentation must contain the date that you created the document, and it must contain a plot created with Plotly.All other coursework projects completed as part of this course can be found at my GitHub repository for this course.

Rationale

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in December 2019 in Wuhan, the capital of China’s Hubei province, and has since spread globally, resulting in the ongoing 2019–20 coronavirus pandemic. For this coursework project, I have opted to use Plotly to illustrate the spread of the Novel Coronavirus across CARICOM Member States. All CARICOM countries are classified as developing countries. They are all relatively small in terms of population and size, and diverse in terms of geography and population, culture and levels of economic and social development. While the pandemic was slow to reach the CARICOM region, the begining of March saw the onset of the pandemic among CARICOM member states.

Data Sources

With a view to map the spread of the disease thus far, I have elected to use two main data sources. Firstly, to obtain the most current data on the incidence of COVID-19, I have opted to utilise the data colelcted by the Johns Hopkins Coronavirus Resource Centre. The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE is compiled from a cross section of sources daily. To supplement this data with relevant socio-demographic data, I have opted to utilise the World Development Indicator Database maintained by the World Bank Group. The World Development Indicators is a compilation of relevant, high-quality, and internationally comparable statistics about global development and the fight against poverty. The database contains 1,600 time series indicators for 217 economies and more than 40 country groups, with data for many indicators going back more than 50 years.

Data Cleaning

A number of specialised data cleaning scripts were prepared to garner current data on a range of issues. These scripts can be found in the GitHub repository created to store the content and code generated in the completion of this course.

Developer

Yohance Nicholas | Consultant Economist @ Kairi Consultants Limited | LinkedIn | GitHub